Overview

Dataset statistics

Number of variables22
Number of observations80829
Missing cells59574
Missing cells (%)3.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.6 MiB
Average record size in memory176.0 B

Variable types

DateTime2
Numeric12
Categorical6
Text2

Alerts

depth is highly overall correlated with depthErrorHigh correlation
depthError is highly overall correlated with depthHigh correlation
dmin is highly overall correlated with horizontalError and 1 other fieldsHigh correlation
gap is highly overall correlated with statusHigh correlation
horizontalError is highly overall correlated with dmin and 1 other fieldsHigh correlation
locationSource is highly overall correlated with magSource and 1 other fieldsHigh correlation
mag is highly overall correlated with nstHigh correlation
magError is highly overall correlated with magNst and 2 other fieldsHigh correlation
magNst is highly overall correlated with magError and 2 other fieldsHigh correlation
magSource is highly overall correlated with locationSource and 1 other fieldsHigh correlation
net is highly overall correlated with locationSource and 1 other fieldsHigh correlation
nst is highly overall correlated with mag and 3 other fieldsHigh correlation
status is highly overall correlated with dmin and 5 other fieldsHigh correlation
magType is highly imbalanced (77.5%)Imbalance
net is highly imbalanced (97.4%)Imbalance
type is highly imbalanced (99.5%)Imbalance
status is highly imbalanced (> 99.9%)Imbalance
locationSource is highly imbalanced (97.1%)Imbalance
magSource is highly imbalanced (96.5%)Imbalance
nst has 53469 (66.2%) missing valuesMissing
magError has 2233 (2.8%) missing valuesMissing
magNst has 2046 (2.5%) missing valuesMissing
id has unique valuesUnique

Reproduction

Analysis started2026-01-05 16:39:56.287717
Analysis finished2026-01-05 16:40:19.087979
Duration22.8 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

time
Date

Distinct80828
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Memory size631.6 KiB
Minimum2015-01-01 05:01:10.640000+00:00
Maximum2025-12-09 21:00:16.498000+00:00
Invalid dates0
Invalid dates (%)0.0%
2026-01-05T16:40:19.177167image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:19.285075image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

latitude
Real number (ℝ)

Distinct77072
Distinct (%)95.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.52536255
Minimum-79.9837
Maximum87.386
Zeros0
Zeros (%)0.0%
Negative44228
Negative (%)54.7%
Memory size631.6 KiB
2026-01-05T16:40:19.390322image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-79.9837
5-th percentile-55.89616
Q1-21.8109
median-4.2178
Q323.1404
95-th percentile51.8531
Maximum87.386
Range167.3697
Interquartile range (IQR)44.9513

Descriptive statistics

Standard deviation30.42698
Coefficient of variation (CV)-57.916157
Kurtosis-0.58193398
Mean-0.52536255
Median Absolute Deviation (MAD)19.767
Skewness0.10050377
Sum-42464.529
Variance925.8011
MonotonicityNot monotonic
2026-01-05T16:40:19.493042image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19.4059
 
< 0.1%
18.43354
 
< 0.1%
-6.2524
 
< 0.1%
-6.48514
 
< 0.1%
-23.68453
 
< 0.1%
-21.25663
 
< 0.1%
-6.12133
 
< 0.1%
-6.18153
 
< 0.1%
-5.9653
 
< 0.1%
-24.18433
 
< 0.1%
Other values (77062)80790
> 99.9%
ValueCountFrequency (%)
-79.98371
< 0.1%
-73.22041
< 0.1%
-71.73391
< 0.1%
-69.77391
< 0.1%
-65.88531
< 0.1%
-65.84971
< 0.1%
-65.77211
< 0.1%
-65.66381
< 0.1%
-65.60881
< 0.1%
-65.60711
< 0.1%
ValueCountFrequency (%)
87.3861
< 0.1%
87.37521
< 0.1%
87.08151
< 0.1%
86.92291
< 0.1%
86.89721
< 0.1%
86.89321
< 0.1%
86.87521
< 0.1%
86.52951
< 0.1%
86.32721
< 0.1%
86.26581
< 0.1%

longitude
Real number (ℝ)

Distinct77634
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.582477
Minimum-179.9997
Maximum179.9993
Zeros0
Zeros (%)0.0%
Negative31151
Negative (%)38.5%
Memory size631.6 KiB
2026-01-05T16:40:19.609619image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-179.9997
5-th percentile-177.78102
Q1-71.5979
median94.8662
Q3142.1823
95-th percentile168.8866
Maximum179.9993
Range359.999
Interquartile range (IQR)213.7802

Descriptive statistics

Standard deviation122.61314
Coefficient of variation (CV)3.3516904
Kurtosis-1.1810853
Mean36.582477
Median Absolute Deviation (MAD)66.8995
Skewness-0.57909466
Sum2956925.1
Variance15033.982
MonotonicityNot monotonic
2026-01-05T16:40:19.713835image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-155.2819
 
< 0.1%
126.42484
 
< 0.1%
-178.02964
 
< 0.1%
-177.1994
 
< 0.1%
-177.37984
 
< 0.1%
-177.17794
 
< 0.1%
127.10144
 
< 0.1%
-155.28166674
 
< 0.1%
-178.00584
 
< 0.1%
126.72954
 
< 0.1%
Other values (77624)80784
99.9%
ValueCountFrequency (%)
-179.99971
< 0.1%
-179.99852
< 0.1%
-179.99831
< 0.1%
-179.99821
< 0.1%
-179.99691
< 0.1%
-179.99631
< 0.1%
-179.99581
< 0.1%
-179.99531
< 0.1%
-179.99442
< 0.1%
-179.99161
< 0.1%
ValueCountFrequency (%)
179.99931
< 0.1%
179.99841
< 0.1%
179.99811
< 0.1%
179.9971
< 0.1%
179.99621
< 0.1%
179.99611
< 0.1%
179.99581
< 0.1%
179.99521
< 0.1%
179.99461
< 0.1%
179.99312
< 0.1%

depth
Real number (ℝ)

High correlation 

Distinct25778
Distinct (%)31.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.09989
Minimum-1.01
Maximum683.36
Zeros10
Zeros (%)< 0.1%
Negative12
Negative (%)< 0.1%
Memory size631.6 KiB
2026-01-05T16:40:19.817086image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1.01
5-th percentile10
Q110
median13.66
Q357.292
95-th percentile256.228
Maximum683.36
Range684.37
Interquartile range (IQR)47.292

Descriptive statistics

Standard deviation113.44141
Coefficient of variation (CV)1.826757
Kurtosis11.576786
Mean62.09989
Median Absolute Deviation (MAD)4.29
Skewness3.3809971
Sum5019472
Variance12868.953
MonotonicityNot monotonic
2026-01-05T16:40:19.924605image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1036248
44.8%
353604
 
4.5%
8124
 
0.2%
9117
 
0.1%
5100
 
0.1%
1183
 
0.1%
2082
 
0.1%
1377
 
0.1%
1275
 
0.1%
1967
 
0.1%
Other values (25768)40252
49.8%
ValueCountFrequency (%)
-1.011
< 0.1%
-0.971
< 0.1%
-0.831
< 0.1%
-0.571
< 0.1%
-0.51
< 0.1%
-0.471
< 0.1%
-0.361
< 0.1%
-0.271
< 0.1%
-0.211
< 0.1%
-0.121
< 0.1%
ValueCountFrequency (%)
683.361
< 0.1%
677.641
< 0.1%
673.061
< 0.1%
670.811
< 0.1%
670.551
< 0.1%
669.5561
< 0.1%
668.231
< 0.1%
667.391
< 0.1%
666.761
< 0.1%
664.741
< 0.1%

mag
Real number (ℝ)

High correlation 

Distinct120
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.804183
Minimum4.5
Maximum8.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size631.6 KiB
2026-01-05T16:40:20.033592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4.5
5-th percentile4.5
Q14.5
median4.7
Q34.9
95-th percentile5.5
Maximum8.8
Range4.3
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.37111561
Coefficient of variation (CV)0.077248433
Kurtosis8.3070397
Mean4.804183
Median Absolute Deviation (MAD)0.2
Skewness2.3370577
Sum388317.3
Variance0.13772679
MonotonicityNot monotonic
2026-01-05T16:40:20.439121image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.520562
25.4%
4.616346
20.2%
4.710212
12.6%
4.97440
 
9.2%
4.87235
 
9.0%
54688
 
5.8%
5.13489
 
4.3%
5.22508
 
3.1%
5.31819
 
2.3%
5.41356
 
1.7%
Other values (110)5174
 
6.4%
ValueCountFrequency (%)
4.520562
25.4%
4.513
 
< 0.1%
4.525
 
< 0.1%
4.536
 
< 0.1%
4.548
 
< 0.1%
4.554
 
< 0.1%
4.563
 
< 0.1%
4.576
 
< 0.1%
4.588
 
< 0.1%
4.595
 
< 0.1%
ValueCountFrequency (%)
8.81
 
< 0.1%
8.31
 
< 0.1%
8.23
 
< 0.1%
8.12
 
< 0.1%
81
 
< 0.1%
7.94
 
< 0.1%
7.89
< 0.1%
7.76
< 0.1%
7.613
< 0.1%
7.514
< 0.1%

magType
Categorical

Imbalance 

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size631.6 KiB
mb
66139 
mww
11568 
mwr
 
2163
ml
 
336
mw
 
251
Other values (10)
 
372

Length

Max length10
Median length2
Mean length2.1743063
Min length2

Characters and Unicode

Total characters175747
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowmb
2nd rowmb
3rd rowmb
4th rowmb
5th rowmb

Common Values

ValueCountFrequency (%)
mb66139
81.8%
mww11568
 
14.3%
mwr2163
 
2.7%
ml336
 
0.4%
mw251
 
0.3%
mwb237
 
0.3%
mwc85
 
0.1%
md19
 
< 0.1%
mlr14
 
< 0.1%
mwp8
 
< 0.1%
Other values (5)9
 
< 0.1%

Length

2026-01-05T16:40:20.531252image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
mb66139
81.8%
mww11568
 
14.3%
mwr2163
 
2.7%
ml339
 
0.4%
mw251
 
0.3%
mwb237
 
0.3%
mwc85
 
0.1%
md22
 
< 0.1%
mlr14
 
< 0.1%
mwp8
 
< 0.1%
Other values (3)3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
m80823
46.0%
b66376
37.8%
w25880
 
14.7%
r2177
 
1.2%
l354
 
0.2%
c85
 
< 0.1%
d22
 
< 0.1%
p8
 
< 0.1%
M6
 
< 0.1%
s2
 
< 0.1%
Other values (10)14
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)175747
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
m80823
46.0%
b66376
37.8%
w25880
 
14.7%
r2177
 
1.2%
l354
 
0.2%
c85
 
< 0.1%
d22
 
< 0.1%
p8
 
< 0.1%
M6
 
< 0.1%
s2
 
< 0.1%
Other values (10)14
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)175747
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
m80823
46.0%
b66376
37.8%
w25880
 
14.7%
r2177
 
1.2%
l354
 
0.2%
c85
 
< 0.1%
d22
 
< 0.1%
p8
 
< 0.1%
M6
 
< 0.1%
s2
 
< 0.1%
Other values (10)14
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)175747
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
m80823
46.0%
b66376
37.8%
w25880
 
14.7%
r2177
 
1.2%
l354
 
0.2%
c85
 
< 0.1%
d22
 
< 0.1%
p8
 
< 0.1%
M6
 
< 0.1%
s2
 
< 0.1%
Other values (10)14
 
< 0.1%

nst
Real number (ℝ)

High correlation  Missing 

Distinct352
Distinct (%)1.3%
Missing53469
Missing (%)66.2%
Infinite0
Infinite (%)0.0%
Mean69.063962
Minimum0
Maximum619
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size631.6 KiB
2026-01-05T16:40:20.634013image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile18
Q134
median56
Q390
95-th percentile165
Maximum619
Range619
Interquartile range (IQR)56

Descriptive statistics

Standard deviation49.729273
Coefficient of variation (CV)0.72004662
Kurtosis6.0349337
Mean69.063962
Median Absolute Deviation (MAD)26
Skewness1.9320155
Sum1889590
Variance2473.0006
MonotonicityNot monotonic
2026-01-05T16:40:20.738064image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34398
 
0.5%
27379
 
0.5%
31378
 
0.5%
24374
 
0.5%
28371
 
0.5%
39370
 
0.5%
38364
 
0.5%
23359
 
0.4%
29359
 
0.4%
26358
 
0.4%
Other values (342)23650
29.3%
(Missing)53469
66.2%
ValueCountFrequency (%)
03
 
< 0.1%
64
 
< 0.1%
78
 
< 0.1%
87
 
< 0.1%
916
 
< 0.1%
1032
 
< 0.1%
1157
0.1%
12105
0.1%
13134
0.2%
14141
0.2%
ValueCountFrequency (%)
6191
< 0.1%
5661
< 0.1%
4751
< 0.1%
4661
< 0.1%
4521
< 0.1%
4441
< 0.1%
4371
< 0.1%
4231
< 0.1%
4201
< 0.1%
4101
< 0.1%

gap
Real number (ℝ)

High correlation 

Distinct348
Distinct (%)0.4%
Missing690
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean92.480061
Minimum7
Maximum348
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size631.6 KiB
2026-01-05T16:40:20.838995image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile30
Q159
median87
Q3121
95-th percentile173
Maximum348
Range341
Interquartile range (IQR)62

Descriptive statistics

Standard deviation44.341165
Coefficient of variation (CV)0.4794673
Kurtosis0.55185841
Mean92.480061
Median Absolute Deviation (MAD)31
Skewness0.698816
Sum7411259.6
Variance1966.1389
MonotonicityNot monotonic
2026-01-05T16:40:20.947151image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
69868
 
1.1%
76808
 
1.0%
70779
 
1.0%
68759
 
0.9%
75744
 
0.9%
64741
 
0.9%
67741
 
0.9%
65729
 
0.9%
72729
 
0.9%
71720
 
0.9%
Other values (338)72521
89.7%
ValueCountFrequency (%)
71
 
< 0.1%
85
 
< 0.1%
96
 
< 0.1%
1025
 
< 0.1%
1134
 
< 0.1%
1249
0.1%
1379
0.1%
13.682067871
 
< 0.1%
1478
0.1%
1591
0.1%
ValueCountFrequency (%)
3481
< 0.1%
3401
< 0.1%
334.81
< 0.1%
3291
< 0.1%
3232
< 0.1%
3211
< 0.1%
3191
< 0.1%
3171
< 0.1%
3131
< 0.1%
3111
< 0.1%

dmin
Real number (ℝ)

High correlation 

Distinct15584
Distinct (%)19.5%
Missing733
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean4.3822528
Minimum0
Maximum62.626
Zeros8
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size631.6 KiB
2026-01-05T16:40:21.056423image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.44475
Q11.333
median2.556
Q35.009
95-th percentile15.92125
Maximum62.626
Range62.626
Interquartile range (IQR)3.676

Descriptive statistics

Standard deviation5.6159568
Coefficient of variation (CV)1.2815228
Kurtosis14.568621
Mean4.3822528
Median Absolute Deviation (MAD)1.511
Skewness3.3518457
Sum351000.92
Variance31.538971
MonotonicityNot monotonic
2026-01-05T16:40:21.159357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.31232
 
< 0.1%
1.11932
 
< 0.1%
1.5331
 
< 0.1%
1.44931
 
< 0.1%
1.24631
 
< 0.1%
1.04630
 
< 0.1%
0.76930
 
< 0.1%
0.97430
 
< 0.1%
1.6330
 
< 0.1%
1.18130
 
< 0.1%
Other values (15574)79789
98.7%
(Missing)733
 
0.9%
ValueCountFrequency (%)
08
< 0.1%
0.0011
 
< 0.1%
0.0013761
 
< 0.1%
0.0022461
 
< 0.1%
0.0028431
 
< 0.1%
0.003091
 
< 0.1%
0.0031681
 
< 0.1%
0.0032151
 
< 0.1%
0.0038451
 
< 0.1%
0.0039251
 
< 0.1%
ValueCountFrequency (%)
62.6261
< 0.1%
62.5581
< 0.1%
58.5281
< 0.1%
58.3791
< 0.1%
57.2851
< 0.1%
56.8961
< 0.1%
56.6231
< 0.1%
55.8981
< 0.1%
55.071
< 0.1%
54.0941
< 0.1%

rms
Real number (ℝ)

Distinct226
Distinct (%)0.3%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.7841621
Minimum0
Maximum2.82
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size631.6 KiB
2026-01-05T16:40:21.258818image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.43
Q10.62
median0.76
Q30.93
95-th percentile1.22
Maximum2.82
Range2.82
Interquartile range (IQR)0.31

Descriptive statistics

Standard deviation0.23905559
Coefficient of variation (CV)0.30485481
Kurtosis0.23920733
Mean0.7841621
Median Absolute Deviation (MAD)0.16
Skewness0.39074989
Sum63381.47
Variance0.057147573
MonotonicityNot monotonic
2026-01-05T16:40:21.364345image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.71437
 
1.8%
0.671412
 
1.7%
0.691406
 
1.7%
0.771395
 
1.7%
0.681389
 
1.7%
0.741388
 
1.7%
0.731383
 
1.7%
0.721380
 
1.7%
0.711363
 
1.7%
0.761362
 
1.7%
Other values (216)66912
82.8%
ValueCountFrequency (%)
01
 
< 0.1%
0.063
 
< 0.1%
0.074
 
< 0.1%
0.088
 
< 0.1%
0.0914
< 0.1%
0.133
< 0.1%
0.1120
< 0.1%
0.11631
 
< 0.1%
0.11651
 
< 0.1%
0.1226
< 0.1%
ValueCountFrequency (%)
2.821
 
< 0.1%
2.591
 
< 0.1%
2.531
 
< 0.1%
2.441
 
< 0.1%
2.421
 
< 0.1%
2.181
 
< 0.1%
2.141
 
< 0.1%
21
 
< 0.1%
1.961
 
< 0.1%
1.953
< 0.1%

net
Categorical

High correlation  Imbalance 

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size631.6 KiB
us
80061 
ak
 
361
hv
 
104
pr
 
97
ci
 
73
Other values (9)
 
133

Length

Max length6
Median length2
Mean length2.0001361
Min length2

Characters and Unicode

Total characters161669
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowus
2nd rowus
3rd rowus
4th rowus
5th rowus

Common Values

ValueCountFrequency (%)
us80061
99.0%
ak361
 
0.4%
hv104
 
0.1%
pr97
 
0.1%
ci73
 
0.1%
nc58
 
0.1%
nn40
 
< 0.1%
tx22
 
< 0.1%
uw4
 
< 0.1%
uu3
 
< 0.1%
Other values (4)6
 
< 0.1%

Length

2026-01-05T16:40:21.460548image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
us80061
99.0%
ak361
 
0.4%
hv104
 
0.1%
pr97
 
0.1%
ci73
 
0.1%
nc58
 
0.1%
nn40
 
< 0.1%
tx22
 
< 0.1%
uw4
 
< 0.1%
uu3
 
< 0.1%
Other values (4)6
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
u80071
49.5%
s80065
49.5%
a363
 
0.2%
k363
 
0.2%
n138
 
0.1%
c134
 
0.1%
v104
 
0.1%
h104
 
0.1%
r97
 
0.1%
p97
 
0.1%
Other values (8)133
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)161669
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
u80071
49.5%
s80065
49.5%
a363
 
0.2%
k363
 
0.2%
n138
 
0.1%
c134
 
0.1%
v104
 
0.1%
h104
 
0.1%
r97
 
0.1%
p97
 
0.1%
Other values (8)133
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)161669
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
u80071
49.5%
s80065
49.5%
a363
 
0.2%
k363
 
0.2%
n138
 
0.1%
c134
 
0.1%
v104
 
0.1%
h104
 
0.1%
r97
 
0.1%
p97
 
0.1%
Other values (8)133
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)161669
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
u80071
49.5%
s80065
49.5%
a363
 
0.2%
k363
 
0.2%
n138
 
0.1%
c134
 
0.1%
v104
 
0.1%
h104
 
0.1%
r97
 
0.1%
p97
 
0.1%
Other values (8)133
 
0.1%

id
Text

Unique 

Distinct80829
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size631.6 KiB
2026-01-05T16:40:21.712985image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length15
Median length10
Mean length10.011085
Min length10

Characters and Unicode

Total characters809186
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique80829 ?
Unique (%)100.0%

Sample

1st rowus10004aif
2nd rowus10004ahx
3rd rowus10004agf
4th rowus10004bu1
5th rowus10004btx
ValueCountFrequency (%)
us10004aci1
 
< 0.1%
us6000pjqz1
 
< 0.1%
us10004aif1
 
< 0.1%
us10004ahx1
 
< 0.1%
us10004agf1
 
< 0.1%
us10004bu11
 
< 0.1%
us10004btx1
 
< 0.1%
us10004aep1
 
< 0.1%
us10004bu01
 
< 0.1%
us10004aeg1
 
< 0.1%
Other values (80819)80819
> 99.9%
2026-01-05T16:40:22.042551image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0248128
30.7%
u86750
 
10.7%
s86619
 
10.7%
736918
 
4.6%
634182
 
4.2%
223381
 
2.9%
123360
 
2.9%
d12151
 
1.5%
h11918
 
1.5%
j11363
 
1.4%
Other values (26)234416
29.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)809186
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0248128
30.7%
u86750
 
10.7%
s86619
 
10.7%
736918
 
4.6%
634182
 
4.2%
223381
 
2.9%
123360
 
2.9%
d12151
 
1.5%
h11918
 
1.5%
j11363
 
1.4%
Other values (26)234416
29.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)809186
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0248128
30.7%
u86750
 
10.7%
s86619
 
10.7%
736918
 
4.6%
634182
 
4.2%
223381
 
2.9%
123360
 
2.9%
d12151
 
1.5%
h11918
 
1.5%
j11363
 
1.4%
Other values (26)234416
29.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)809186
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0248128
30.7%
u86750
 
10.7%
s86619
 
10.7%
736918
 
4.6%
634182
 
4.2%
223381
 
2.9%
123360
 
2.9%
d12151
 
1.5%
h11918
 
1.5%
j11363
 
1.4%
Other values (26)234416
29.0%

updated
Date

Distinct30712
Distinct (%)38.0%
Missing0
Missing (%)0.0%
Memory size631.6 KiB
Minimum2015-03-27 07:00:55.040000+00:00
Maximum2025-12-10 00:40:56.468000+00:00
Invalid dates0
Invalid dates (%)0.0%
2026-01-05T16:40:22.144354image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:22.251227image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

place
Text

Distinct45286
Distinct (%)56.0%
Missing0
Missing (%)0.0%
Memory size631.6 KiB
2026-01-05T16:40:22.592936image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length65
Median length56
Mean length29.218152
Min length4

Characters and Unicode

Total characters2361674
Distinct characters136
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique38448 ?
Unique (%)47.6%

Sample

1st row101 km SE of Gorontalo, Indonesia
2nd row52 km W of Ovalle, Chile
3rd row61 km ENE of Takaka, New Zealand
4th rowsouth of Tonga
5th row70 km NW of Panguna, Papua New Guinea
ValueCountFrequency (%)
of60672
 
13.5%
km54583
 
12.2%
islands16147
 
3.6%
region10631
 
2.4%
indonesia7872
 
1.8%
new7369
 
1.6%
south7141
 
1.6%
japan6126
 
1.4%
ridge4905
 
1.1%
ese4667
 
1.0%
Other values (5882)269019
59.9%
2026-01-05T16:40:23.049428image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
368305
 
15.6%
a191950
 
8.1%
o146390
 
6.2%
n132708
 
5.6%
i118147
 
5.0%
e107895
 
4.6%
s85759
 
3.6%
m72300
 
3.1%
k70734
 
3.0%
f66744
 
2.8%
Other values (126)1000742
42.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)2361674
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
368305
 
15.6%
a191950
 
8.1%
o146390
 
6.2%
n132708
 
5.6%
i118147
 
5.0%
e107895
 
4.6%
s85759
 
3.6%
m72300
 
3.1%
k70734
 
3.0%
f66744
 
2.8%
Other values (126)1000742
42.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2361674
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
368305
 
15.6%
a191950
 
8.1%
o146390
 
6.2%
n132708
 
5.6%
i118147
 
5.0%
e107895
 
4.6%
s85759
 
3.6%
m72300
 
3.1%
k70734
 
3.0%
f66744
 
2.8%
Other values (126)1000742
42.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2361674
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
368305
 
15.6%
a191950
 
8.1%
o146390
 
6.2%
n132708
 
5.6%
i118147
 
5.0%
e107895
 
4.6%
s85759
 
3.6%
m72300
 
3.1%
k70734
 
3.0%
f66744
 
2.8%
Other values (126)1000742
42.4%

type
Categorical

Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size631.6 KiB
earthquake
80752 
volcanic eruption
 
71
nuclear explosion
 
3
landslide
 
2
mine collapse
 
1

Length

Max length17
Median length10
Mean length10.006421
Min length9

Characters and Unicode

Total characters808809
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowearthquake
2nd rowearthquake
3rd rowearthquake
4th rowearthquake
5th rowearthquake

Common Values

ValueCountFrequency (%)
earthquake80752
99.9%
volcanic eruption71
 
0.1%
nuclear explosion3
 
< 0.1%
landslide2
 
< 0.1%
mine collapse1
 
< 0.1%

Length

2026-01-05T16:40:23.149556image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-05T16:40:23.224166image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
earthquake80752
99.8%
volcanic71
 
0.1%
eruption71
 
0.1%
nuclear3
 
< 0.1%
explosion3
 
< 0.1%
landslide2
 
< 0.1%
mine1
 
< 0.1%
collapse1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e161585
20.0%
a161581
20.0%
r80826
10.0%
u80826
10.0%
t80823
10.0%
h80752
10.0%
q80752
10.0%
k80752
10.0%
n151
 
< 0.1%
o149
 
< 0.1%
Other values (10)612
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)808809
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e161585
20.0%
a161581
20.0%
r80826
10.0%
u80826
10.0%
t80823
10.0%
h80752
10.0%
q80752
10.0%
k80752
10.0%
n151
 
< 0.1%
o149
 
< 0.1%
Other values (10)612
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)808809
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e161585
20.0%
a161581
20.0%
r80826
10.0%
u80826
10.0%
t80823
10.0%
h80752
10.0%
q80752
10.0%
k80752
10.0%
n151
 
< 0.1%
o149
 
< 0.1%
Other values (10)612
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)808809
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e161585
20.0%
a161581
20.0%
r80826
10.0%
u80826
10.0%
t80823
10.0%
h80752
10.0%
q80752
10.0%
k80752
10.0%
n151
 
< 0.1%
o149
 
< 0.1%
Other values (10)612
 
0.1%

horizontalError
Real number (ℝ)

High correlation 

Distinct1745
Distinct (%)2.2%
Missing401
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean8.4219411
Minimum0
Maximum51.7
Zeros5
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size631.6 KiB
2026-01-05T16:40:23.316049image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q16.4
median8.12
Q310.27
95-th percentile13.7365
Maximum51.7
Range51.7
Interquartile range (IQR)3.87

Descriptive statistics

Standard deviation2.9774726
Coefficient of variation (CV)0.35353758
Kurtosis1.497187
Mean8.4219411
Median Absolute Deviation (MAD)1.92
Skewness0.5363052
Sum677359.88
Variance8.8653434
MonotonicityNot monotonic
2026-01-05T16:40:23.428605image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.4892
 
1.1%
7.5885
 
1.1%
7.2872
 
1.1%
6.9872
 
1.1%
7.9870
 
1.1%
7.7864
 
1.1%
7.8862
 
1.1%
7.3857
 
1.1%
7.6856
 
1.1%
8852
 
1.1%
Other values (1735)71746
88.8%
ValueCountFrequency (%)
05
 
< 0.1%
0.082
 
< 0.1%
0.094
 
< 0.1%
0.13
 
< 0.1%
0.118
 
< 0.1%
0.1222
< 0.1%
0.1322
< 0.1%
0.1417
< 0.1%
0.156
 
< 0.1%
0.167
 
< 0.1%
ValueCountFrequency (%)
51.71
< 0.1%
37.61
< 0.1%
35.31
< 0.1%
34.61
< 0.1%
32.81
< 0.1%
31.71
< 0.1%
31.51
< 0.1%
29.21
< 0.1%
29.11
< 0.1%
28.71
< 0.1%

depthError
Real number (ℝ)

High correlation 

Distinct6608
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8505894
Minimum0
Maximum61.9
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size631.6 KiB
2026-01-05T16:40:23.532399image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.7
Q11.881
median2
Q35.7
95-th percentile8.5
Maximum61.9
Range61.9
Interquartile range (IQR)3.819

Descriptive statistics

Standard deviation2.7291857
Coefficient of variation (CV)0.70877088
Kurtosis13.137914
Mean3.8505894
Median Absolute Deviation (MAD)0.3
Skewness2.0444498
Sum311239.29
Variance7.4484544
MonotonicityNot monotonic
2026-01-05T16:40:23.637403image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.914617
 
18.1%
1.87710
 
9.5%
22968
 
3.7%
1.72108
 
2.6%
4.9555
 
0.7%
5.5540
 
0.7%
1.6529
 
0.7%
5.8522
 
0.6%
5.6517
 
0.6%
5.4507
 
0.6%
Other values (6598)50256
62.2%
ValueCountFrequency (%)
04
 
< 0.1%
0.081
 
< 0.1%
0.094
 
< 0.1%
0.155
0.1%
0.118
 
< 0.1%
0.129
 
< 0.1%
0.132
 
< 0.1%
0.141
 
< 0.1%
0.152
 
< 0.1%
0.165
 
< 0.1%
ValueCountFrequency (%)
61.91
 
< 0.1%
57.11
 
< 0.1%
43.51
 
< 0.1%
41.71
 
< 0.1%
38.71
 
< 0.1%
32.571
 
< 0.1%
32.2291
 
< 0.1%
31.6135
< 0.1%
31.61
 
< 0.1%
29.861
 
< 0.1%

magError
Real number (ℝ)

High correlation  Missing 

Distinct324
Distinct (%)0.4%
Missing2233
Missing (%)2.8%
Infinite0
Infinite (%)0.0%
Mean0.09442515
Minimum0
Maximum0.91
Zeros23
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size631.6 KiB
2026-01-05T16:40:23.759756image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.037
Q10.061
median0.086
Q30.118
95-th percentile0.181
Maximum0.91
Range0.91
Interquartile range (IQR)0.057

Descriptive statistics

Standard deviation0.047371475
Coefficient of variation (CV)0.50168282
Kurtosis7.0022462
Mean0.09442515
Median Absolute Deviation (MAD)0.028
Skewness1.6705223
Sum7421.4391
Variance0.0022440567
MonotonicityNot monotonic
2026-01-05T16:40:23.869362image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0981255
 
1.6%
0.0931172
 
1.4%
0.0891121
 
1.4%
0.0731060
 
1.3%
0.0831057
 
1.3%
0.081041
 
1.3%
0.0861037
 
1.3%
0.0691021
 
1.3%
0.0711008
 
1.2%
0.078991
 
1.2%
Other values (314)67833
83.9%
(Missing)2233
 
2.8%
ValueCountFrequency (%)
023
 
< 0.1%
0.0182
 
< 0.1%
0.01910
 
< 0.1%
0.0214
 
< 0.1%
0.02130
 
< 0.1%
0.02243
 
0.1%
0.02375
0.1%
0.024109
0.1%
0.025139
0.2%
0.026138
0.2%
ValueCountFrequency (%)
0.911
 
< 0.1%
0.61
 
< 0.1%
0.5631
 
< 0.1%
0.5581
 
< 0.1%
0.5473
< 0.1%
0.5462
< 0.1%
0.5451
 
< 0.1%
0.5443
< 0.1%
0.5422
< 0.1%
0.5412
< 0.1%

magNst
Real number (ℝ)

High correlation  Missing 

Distinct695
Distinct (%)0.9%
Missing2046
Missing (%)2.5%
Infinite0
Infinite (%)0.0%
Mean59.930658
Minimum0
Maximum1027
Zeros22
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size631.6 KiB
2026-01-05T16:40:23.975038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9
Q118
median33
Q367
95-th percentile209
Maximum1027
Range1027
Interquartile range (IQR)49

Descriptive statistics

Standard deviation78.052823
Coefficient of variation (CV)1.3023856
Kurtosis18.007074
Mean59.930658
Median Absolute Deviation (MAD)19
Skewness3.5883745
Sum4721517
Variance6092.2432
MonotonicityNot monotonic
2026-01-05T16:40:24.084018image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
101899
 
2.3%
121882
 
2.3%
141864
 
2.3%
131829
 
2.3%
181803
 
2.2%
151783
 
2.2%
111752
 
2.2%
161713
 
2.1%
191658
 
2.1%
171613
 
2.0%
Other values (685)60987
75.5%
(Missing)2046
 
2.5%
ValueCountFrequency (%)
022
 
< 0.1%
113
 
< 0.1%
258
 
0.1%
3139
 
0.2%
4267
 
0.3%
5365
 
0.5%
6597
0.7%
7703
0.9%
8993
1.2%
91191
1.5%
ValueCountFrequency (%)
10271
< 0.1%
10041
< 0.1%
9871
< 0.1%
9771
< 0.1%
9751
< 0.1%
9541
< 0.1%
9411
< 0.1%
8961
< 0.1%
8841
< 0.1%
8791
< 0.1%

status
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size631.6 KiB
reviewed
80828 
automatic
 
1

Length

Max length9
Median length8
Mean length8.0000124
Min length8

Characters and Unicode

Total characters646633
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowreviewed
2nd rowreviewed
3rd rowreviewed
4th rowreviewed
5th rowreviewed

Common Values

ValueCountFrequency (%)
reviewed80828
> 99.9%
automatic1
 
< 0.1%

Length

2026-01-05T16:40:24.174075image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-05T16:40:24.223884image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
reviewed80828
> 99.9%
automatic1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e242484
37.5%
i80829
 
12.5%
r80828
 
12.5%
v80828
 
12.5%
w80828
 
12.5%
d80828
 
12.5%
a2
 
< 0.1%
t2
 
< 0.1%
u1
 
< 0.1%
o1
 
< 0.1%
Other values (2)2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)646633
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e242484
37.5%
i80829
 
12.5%
r80828
 
12.5%
v80828
 
12.5%
w80828
 
12.5%
d80828
 
12.5%
a2
 
< 0.1%
t2
 
< 0.1%
u1
 
< 0.1%
o1
 
< 0.1%
Other values (2)2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)646633
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e242484
37.5%
i80829
 
12.5%
r80828
 
12.5%
v80828
 
12.5%
w80828
 
12.5%
d80828
 
12.5%
a2
 
< 0.1%
t2
 
< 0.1%
u1
 
< 0.1%
o1
 
< 0.1%
Other values (2)2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)646633
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e242484
37.5%
i80829
 
12.5%
r80828
 
12.5%
v80828
 
12.5%
w80828
 
12.5%
d80828
 
12.5%
a2
 
< 0.1%
t2
 
< 0.1%
u1
 
< 0.1%
o1
 
< 0.1%
Other values (2)2
 
< 0.1%

locationSource
Categorical

High correlation  Imbalance 

Distinct41
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size631.6 KiB
us
79717 
ak
 
394
guc
 
116
hv
 
104
pr
 
98
Other values (36)
 
400

Length

Max length6
Median length2
Mean length2.0042683
Min length2

Characters and Unicode

Total characters162003
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)< 0.1%

Sample

1st rowus
2nd rowus
3rd rowus
4th rowus
5th rowus

Common Values

ValueCountFrequency (%)
us79717
98.6%
ak394
 
0.5%
guc116
 
0.1%
hv104
 
0.1%
pr98
 
0.1%
ci73
 
0.1%
nc58
 
0.1%
nn40
 
< 0.1%
ath27
 
< 0.1%
tx22
 
< 0.1%
Other values (31)180
 
0.2%

Length

2026-01-05T16:40:24.290698image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
us79717
98.6%
ak394
 
0.5%
guc116
 
0.1%
hv104
 
0.1%
pr98
 
0.1%
ci73
 
0.1%
nc58
 
0.1%
nn40
 
< 0.1%
ath27
 
< 0.1%
tx22
 
< 0.1%
Other values (31)180
 
0.2%

Most occurring characters

ValueCountFrequency (%)
u79886
49.3%
s79766
49.2%
a459
 
0.3%
k404
 
0.2%
c281
 
0.2%
n170
 
0.1%
h149
 
0.1%
g131
 
0.1%
p127
 
0.1%
r122
 
0.1%
Other values (13)508
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)162003
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
u79886
49.3%
s79766
49.2%
a459
 
0.3%
k404
 
0.2%
c281
 
0.2%
n170
 
0.1%
h149
 
0.1%
g131
 
0.1%
p127
 
0.1%
r122
 
0.1%
Other values (13)508
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)162003
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
u79886
49.3%
s79766
49.2%
a459
 
0.3%
k404
 
0.2%
c281
 
0.2%
n170
 
0.1%
h149
 
0.1%
g131
 
0.1%
p127
 
0.1%
r122
 
0.1%
Other values (13)508
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)162003
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
u79886
49.3%
s79766
49.2%
a459
 
0.3%
k404
 
0.2%
c281
 
0.2%
n170
 
0.1%
h149
 
0.1%
g131
 
0.1%
p127
 
0.1%
r122
 
0.1%
Other values (13)508
 
0.3%

magSource
Categorical

High correlation  Imbalance 

Distinct30
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size631.6 KiB
us
79499 
guc
 
380
ak
 
361
hv
 
104
pr
 
97
Other values (25)
 
388

Length

Max length6
Median length2
Mean length2.0082891
Min length2

Characters and Unicode

Total characters162328
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)< 0.1%

Sample

1st rowus
2nd rowus
3rd rowus
4th rowus
5th rowus

Common Values

ValueCountFrequency (%)
us79499
98.4%
guc380
 
0.5%
ak361
 
0.4%
hv104
 
0.1%
pr97
 
0.1%
gcmt84
 
0.1%
ci73
 
0.1%
nc58
 
0.1%
pgc55
 
0.1%
nn40
 
< 0.1%
Other values (20)78
 
0.1%

Length

2026-01-05T16:40:24.384308image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
us79499
98.4%
guc380
 
0.5%
ak361
 
0.4%
hv104
 
0.1%
pr97
 
0.1%
gcmt84
 
0.1%
ci73
 
0.1%
nc58
 
0.1%
pgc55
 
0.1%
nn40
 
< 0.1%
Other values (20)78
 
0.1%

Most occurring characters

ValueCountFrequency (%)
u79899
49.2%
s79515
49.0%
c685
 
0.4%
g521
 
0.3%
a379
 
0.2%
k361
 
0.2%
p153
 
0.1%
n150
 
0.1%
r128
 
0.1%
t110
 
0.1%
Other values (12)427
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)162328
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
u79899
49.2%
s79515
49.0%
c685
 
0.4%
g521
 
0.3%
a379
 
0.2%
k361
 
0.2%
p153
 
0.1%
n150
 
0.1%
r128
 
0.1%
t110
 
0.1%
Other values (12)427
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)162328
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
u79899
49.2%
s79515
49.0%
c685
 
0.4%
g521
 
0.3%
a379
 
0.2%
k361
 
0.2%
p153
 
0.1%
n150
 
0.1%
r128
 
0.1%
t110
 
0.1%
Other values (12)427
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)162328
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
u79899
49.2%
s79515
49.0%
c685
 
0.4%
g521
 
0.3%
a379
 
0.2%
k361
 
0.2%
p153
 
0.1%
n150
 
0.1%
r128
 
0.1%
t110
 
0.1%
Other values (12)427
 
0.3%

Interactions

2026-01-05T16:40:17.121608image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:03.702850image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:04.899287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:05.956058image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:07.040690image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:08.236282image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:09.261920image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:10.560279image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:11.632594image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:12.781905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:14.339131image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:16.093695image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:17.217213image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:03.805803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:04.993016image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:06.059329image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:07.143571image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:08.325295image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:09.353671image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:10.648115image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:11.721281image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:12.911994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:14.470890image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:16.178385image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:17.304390image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:03.894585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:05.089232image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:06.150097image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:07.388234image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:08.408076image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:09.445674image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:10.740195image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:11.807512image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:13.042355image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:14.602836image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:16.267105image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:17.393842image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:04.000767image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:05.178402image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:06.238898image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:07.472208image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:08.489644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:09.534980image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:10.833695image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:11.897485image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:13.177030image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:14.731949image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:16.359429image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:17.470090image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:04.079245image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:05.258376image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:06.324344image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:07.544640image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:08.568198image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:09.615104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:10.911864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:11.973816image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:13.298593image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:14.845164image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:16.431497image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:17.569339image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:04.171153image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:05.350614image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:06.411840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:07.626989image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:08.648937image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:09.707791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:11.002394image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:12.068274image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:13.425661image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:14.969286image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:16.536023image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:17.658858image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:04.263103image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:05.440329image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:06.502656image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:07.714704image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:08.732775image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:09.800283image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:11.093148image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:12.166054image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:13.553790image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:15.433657image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:16.620008image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:17.747614image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:04.351646image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:05.527827image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:06.591599image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:07.797862image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:08.812402image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:09.889656image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:11.181589image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:12.271340image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:13.686513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:15.572518image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:16.706655image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:17.832224image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:04.435534image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:05.613106image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:06.683407image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:07.879347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:08.902349image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:09.989425image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:11.290467image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:12.358305image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:13.819571image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:15.718714image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:16.787324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:17.918800image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:04.521148image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:05.700368image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:06.771932image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:07.960215image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:08.983274image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:10.076753image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:11.377444image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:12.444076image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:13.946119image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:15.835303image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:16.869997image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:18.007170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:04.604847image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:05.787039image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:06.863612image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:08.056442image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:09.068424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:10.168144image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:11.462457image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:12.530645image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:14.081990image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:15.920732image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:16.953960image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:18.093425image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:04.814761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:05.869954image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:06.952711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:08.153004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:09.166895image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:10.269138image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:11.544522image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:12.652058image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:14.203914image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:16.008390image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-05T16:40:17.033180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2026-01-05T16:40:24.464992image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
depthdepthErrordmingaphorizontalErrorlatitudelocationSourcelongitudemagmagErrormagNstmagSourcemagTypenetnstrmsstatustype
depth1.0000.736-0.153-0.0990.065-0.0250.0100.042-0.082-0.1610.1680.0330.0190.0060.1750.0570.0120.000
depthError0.7361.000-0.1430.1810.1170.0030.1210.068-0.3030.0590.0290.2830.1420.123-0.0950.0020.0000.086
dmin-0.153-0.1431.0000.0240.546-0.2920.000-0.0710.0020.119-0.0470.0080.0350.000-0.155-0.1091.0000.000
gap-0.0990.1810.0241.0000.270-0.0240.084-0.022-0.4450.410-0.2540.0880.1820.083-0.492-0.0681.0000.026
horizontalError0.0650.1170.5460.2701.000-0.3100.147-0.046-0.1470.236-0.1220.0640.1120.061-0.294-0.1061.0000.032
latitude-0.0250.003-0.292-0.024-0.3101.0000.1610.262-0.032-0.3360.2950.1610.0890.1550.347-0.0170.0000.037
locationSource0.0100.1210.0000.0840.1470.1611.0000.1550.0000.1110.0120.6570.3400.9960.0300.2580.0450.397
longitude0.0420.068-0.071-0.022-0.0460.2620.1551.0000.002-0.0080.0320.1680.1210.1320.052-0.0580.0000.033
mag-0.082-0.3030.002-0.445-0.147-0.0320.0000.0021.000-0.4810.2600.0340.2420.0130.5540.0880.0000.025
magError-0.1610.0590.1190.4100.236-0.3360.111-0.008-0.4811.000-0.8710.2900.1280.109-0.811-0.0671.0000.000
magNst0.1680.029-0.047-0.254-0.1220.2950.0120.0320.260-0.8711.0000.0000.0580.0000.5380.0481.0000.111
magSource0.0330.2830.0080.0880.0640.1610.6570.1680.0340.2900.0001.0000.4630.9600.0310.2020.0490.361
magType0.0190.1420.0350.1820.1120.0890.3400.1210.2420.1280.0580.4631.0000.3540.1840.2860.0000.427
net0.0060.1230.0000.0830.0610.1550.9960.1320.0130.1090.0000.9600.3541.0000.0300.2010.0510.361
nst0.175-0.095-0.155-0.492-0.2940.3470.0300.0520.554-0.8110.5380.0310.1840.0301.000-0.0111.0000.028
rms0.0570.002-0.109-0.068-0.106-0.0170.258-0.0580.088-0.0670.0480.2020.2860.201-0.0111.0000.0000.136
status0.0120.0001.0001.0001.0000.0000.0450.0000.0001.0001.0000.0490.0000.0511.0000.0001.0000.000
type0.0000.0860.0000.0260.0320.0370.3970.0330.0250.0000.1110.3610.4270.3610.0280.1360.0001.000

Missing values

2026-01-05T16:40:18.280935image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2026-01-05T16:40:18.564733image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2026-01-05T16:40:18.913190image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

timelatitudelongitudedepthmagmagTypenstgapdminrmsnetidupdatedplacetypehorizontalErrordepthErrormagErrormagNststatuslocationSourcemagSource
02015-12-30 23:20:56.840000+00:000.0229123.8194143.054.6mbNaN51.01.4880.83usus10004aif2016-03-18T01:13:08.040Z101 km SE of Gorontalo, Indonesiaearthquake6.46.70.08343.0reviewedusus
12015-12-30 21:29:23.040000+00:00-30.6777-71.734625.624.5mbNaN180.00.0850.94usus10004ahx2016-03-18T01:13:08.040Z52 km W of Ovalle, Chileearthquake3.95.40.1928.0reviewedusus
22015-12-30 19:50:48.210000+00:00-40.5641173.4219142.404.6mbNaN65.00.6530.70usus10004agf2022-08-02T02:00:26.953Z61 km ENE of Takaka, New Zealandearthquake7.36.30.09734.0reviewedusus
32015-12-30 18:26:34.610000+00:00-24.4715-175.871624.214.9mbNaN77.05.0970.99usus10004bu12016-11-10T22:06:33.391Zsouth of Tongaearthquake13.74.70.07457.0reviewedusus
42015-12-30 14:46:47.730000+00:00-5.9568154.9611184.564.5mbNaN94.03.2920.76usus10004btx2016-03-18T01:13:08.040Z70 km NW of Panguna, Papua New Guineaearthquake11.68.20.10129.0reviewedusus
52015-12-30 12:57:36.210000+00:0033.8836137.3211338.234.5mbNaN57.01.0660.75usus10004aep2016-03-18T01:13:08.040Z79 km SSE of Toba, Japanearthquake7.45.80.053103.0reviewedusus
62015-12-30 12:50:12.930000+00:00-18.0505-178.7085607.864.5mbNaN192.03.0980.67usus10004bu02016-03-18T01:13:08.040Z209 km E of Levuka, Fijiearthquake14.212.70.14015.0reviewedusus
72015-12-30 11:53:51.690000+00:0040.9013142.791551.544.5mbNaN134.01.1460.60usus10004aeg2016-03-18T01:13:08.040Z117 km ENE of Hachinohe, Japanearthquake7.57.70.13620.0reviewedusus
82015-12-30 11:08:36+00:00-21.9779-174.470146.155.0mbNaN50.07.8800.85usus10004ae82016-11-10T22:06:32.298ZTongaearthquake8.55.40.043177.0reviewedusus
92015-12-30 10:46:44+00:0019.3214121.127328.554.5mbNaN51.03.4790.95usus10004ae42016-03-18T01:13:08.040Z78 km N of Namuac, Philippinesearthquake7.74.70.08543.0reviewedusus
timelatitudelongitudedepthmagmagTypenstgapdminrmsnetidupdatedplacetypehorizontalErrordepthErrormagErrormagNststatuslocationSourcemagSource
808192025-01-01 04:59:10.638000+00:007.3329-35.408010.0004.5mb30.0118.013.0820.86usus6000pjr52025-03-17T21:34:00.040Zcentral Mid-Atlantic Ridgeearthquake14.581.9370.11024.0reviewedusus
808202025-01-01 04:39:18.208000+00:00-20.9273169.381922.0005.5mww152.081.02.4590.56usus6000pgsd2025-03-17T21:34:00.040Z153 km S of Isangel, Vanuatuearthquake8.051.9090.05829.0reviewedusus
808212025-01-01 04:37:23.062000+00:00-17.6410168.172191.0034.8mb24.0140.02.3711.06usus6000pgse2025-03-17T21:34:00.040Z18 km NW of Port-Vila, Vanuatuearthquake9.898.9260.11623.0reviewedusus
808222025-01-01 04:28:16.938000+00:00-3.9182151.533310.0004.6mb39.088.00.6090.87usus6000pjr42025-03-17T21:34:00.040Z76 km WNW of Rabaul, Papua New Guineaearthquake6.741.8280.10726.0reviewedusus
808232025-01-01 04:02:34.870000+00:0025.3453125.950847.1644.5mb29.0116.01.8320.86usus6000pgs32025-03-17T21:33:59.040Z88 km NE of Hirara, Japanearthquake6.746.6330.12718.0reviewedusus
808242025-01-01 03:58:24.839000+00:00-19.920167.273110.0004.7mb40.073.03.6780.49usus6000pgs42025-03-17T21:33:59.040ZMid-Indian Ridgeearthquake6.151.8460.10229.0reviewedusus
808252025-01-01 03:56:39.832000+00:00-23.0291-66.6081208.9744.9mww90.043.01.4490.92usus6000pgs22025-03-17T21:33:59.040Z97 km WNW of El Aguilar, Argentinaearthquake9.816.2170.05730.0reviewedusus
808262025-01-01 03:27:56.593000+00:009.162940.007110.0004.5mb54.064.01.3380.75usus6000pgs02025-03-17T21:33:59.040Z26 km NW of Āwash, Ethiopiaearthquake7.691.9170.08541.0reviewedusus
808272025-01-01 02:00:06.918000+00:00-3.8521151.620710.0004.5mb36.098.00.5910.98usus6000pjr12025-03-17T21:33:58.040Z71 km WNW of Rabaul, Papua New Guineaearthquake7.801.9010.11622.0reviewedusus
808282025-01-01 01:46:59.538000+00:00-3.8682151.653610.0004.7mb39.099.00.5570.86usus6000pjqz2025-03-17T21:33:58.040Z67 km WNW of Rabaul, Papua New Guineaearthquake6.861.9010.10826.0reviewedusus